Carbon Estimation

Accurate carbon estimation, crucial for environmental monitoring and carbon market integrity, focuses on improving the prediction of soil organic carbon (SOC) and aboveground biomass (AGB) to determine total carbon stocks. Current research emphasizes developing more robust and generalizable models, often employing machine learning techniques like boosted regression trees, random forests, and XGBoost, and exploring the relationships between SOC and AGB to leverage combined data for improved estimations. These advancements aim to provide more reliable and scalable carbon accounting methods, impacting both scientific understanding of carbon cycles and the practical implementation of carbon offsetting programs.